An Approach to Diminish Boundary Distortion in Compressing Grid DEM with Discrete Wavelet Transform

2012 ◽  
Vol 220-223 ◽  
pp. 2617-2621
Author(s):  
Zhan Qiang Chang ◽  
Xiao Meng Liu ◽  
Zu Rui Ao
Keyword(s):  
Wavelet Transform ◽  
Spatial Analysis ◽  
Data Compression ◽  
Discrete Wavelet Transform ◽  
Digital Elevation Model ◽  
High Accuracy ◽  
Discrete Wavelet ◽  
The Core ◽  
Digital Elevation ◽  
Elevation Model

Digital elevation model, the core data for 3-dimensinal visualization and spatial analysis, plays a key role in geosciences and GIS, and wavelet transform is an important tool for data compression. According to the characteristics of DEM data and wavelet transform, we proposed an approach to diminish boundary distortion in compressing grid DEM data with discrete wavelet transform, so that the compression performances are evidently improved. The experimental results indicate that not only higher compression ratios but also reconstructed DEM data with high accuracy are achieved by using the proposed approach.

scholarly journals Automated Conflation of Digital Elevation Model with Reference Hydrographic Lines

2020 ◽  
Vol 9 (5) ◽  
pp. 334
Author(s):  
Timofey E. Samsonov
Keyword(s):  
Spatial Analysis ◽  
Digital Elevation Model ◽  
Spatial Data ◽  
Drainage Network ◽  
Map Generalization ◽  
Data Types ◽  
Digital Elevation ◽  
Elevation Data ◽  
Elevation Model ◽  
Different Sources

Combining misaligned spatial data from different sources complicates spatial analysis and creation of maps. Conflation is a process that solves the misalignment problem through spatial adjustment or attribute transfer between similar features in two datasets. Even though a combination of digital elevation model (DEM) and vector hydrographic lines is a common practice in spatial analysis and mapping, no method for automated conflation between these spatial data types has been developed so far. The problem of DEM and hydrography misalignment arises not only in map compilation, but also during the production of generalized datasets. There is a lack of automated solutions which can ensure that the drainage network represented in the surface of generalized DEM is spatially adjusted with independently generalized vector hydrography. We propose a new method that performs the conflation of DEM with linear hydrographic data and is embeddable into DEM generalization process. Given a set of reference hydrographic lines, our method automatically recognizes the most similar paths on DEM surface called counterpart streams. The elevation data extracted from DEM is then rubbersheeted locally using the links between counterpart streams and reference lines, and the conflated DEM is reconstructed from the rubbersheeted elevation data. The algorithm developed for extraction of counterpart streams ensures that the resulting set of lines comprises the network similar to the network of ordered reference lines. We also show how our approach can be seamlessly integrated into a TIN-based structural DEM generalization process with spatial adjustment to pre-generalized hydrographic lines as additional requirement. The combination of the GEBCO_2019 DEM and the Natural Earth 10M vector dataset is used to illustrate the effectiveness of DEM conflation both in map compilation and map generalization workflows. Resulting maps are geographically correct and are aesthetically more pleasing in comparison to a straightforward combination of misaligned DEM and hydrographic lines without conflation.

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